
概览
主要功能
- Prompt 和插件抽象
- 代理和规划器orchestrations
- 内存和向量存储连接器
- 多语种 SDK 支持
- 与 Azure 和 OpenAI 服务集成
- 监控和依赖注入的钩子
价格
- 模型
- Free
- 评分
- 4.6 / 5 (5)
使用场景
在企业 C# 应用中嵌入 LLM
使用 SDK 整合 OpenAI 或 Azure OpenAI 模型到现有的 .NET 代码库中,视 AI 能力为可以与本机代码协同的可组合函数。
构建多步骤的 AI 代理和规划器
利用规划器和代理抽象来协调提示、插件和工具以生成自动流程,来执行复杂任务。
将记忆和检索添加到 AI 工作流
通过内存抽象连接向量存储,让应用获得上下文相容的回忆,支持 RAG 模式跨支持的模型供应商。
在不更改应用的情况下更换 AI 供应商
使用可替换的模型连接器将 OpenAI、Azure OpenAI 和 Hugging Face 之间切换,减少生产 AI 系统的供应商锁定风险。
优点 & 缺点
优点
- 开源,并由 Microsoft 支持
- 支持 C#、Python 和 Java
- 可替换的模型和内存供应商
- 在现有的企业代码库中衔接
- 无需更改应用
缺点
- 学习曲线较陡于无码工具
- API 已经随着突破性的改变而演化
- 文档可能落后于发布
- 较少的支持资源
评测
5 个评分的平均值。
登录以留下评测。
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on agent and planner orchestration, and open source and backed by Microsoft caught me off guard. APIs have evolved with breaking changes is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Does the job
Pretty happy overall. Multi-language SDK support just works and fits into existing enterprise codebases. Documentation can lag behind releases can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.
Compared a few options
Evaluated this against two competitors. Where it wins: multi-language SDK support and pluggable model and memory providers. On balance the feature set — especially agent and planner orchestration — justifies the 5 stars for our use case.
Solid for our team
We rolled this out across the team last quarter and open source and backed by Microsoft. Agent and planner orchestration fits neatly into how we already work, and agent and planner orchestration removed a step we used to do by hand. Steeper learning curve than no-code tools, which is the main caveat, but it has held up under daily use.
Use it every day
Honestly didn't expect to like it this much. Integration with Azure and OpenAI services is exactly what I needed, and supports C#, Python, and Java. but I reach for it almost every day now and it just clicks.
问答
What is the learning curve like?
Semantic Kernel has a steeper learning curve than no-code tools, but it is designed to be flexible and modular.
Asked by Anders Lindgren · Nov 11, 2025
Is Semantic Kernel suitable for production?
Yes, it is designed for production scenarios with extensibility points for telemetry, dependency injection, and enterprise patterns.
Asked by Freya Solberg · Sep 7, 2025
Can I use different AI models?
Yes, Semantic Kernel allows you to swap between providers like OpenAI, Azure OpenAI, and Hugging Face.
Asked by Larisa Ionescu · Aug 28, 2025
What languages are supported?
Semantic Kernel supports C#, Python, and Java.
Asked by Cristina Moreno · Aug 25, 2025
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